We have described about the business analytics , definition and competitive advantages and different tools of business analytics

1. BUSINESS
ANALYTICS
PR ESEN TED BY :
MAJ J I RESHMA

2. Definition Of Business
Analytics
Business analytics refers to the practice of
analyzing and interpreting data to gain insights and
make informed business decisions. It involves
using statistical and quantitative analysis
techniques to extract meaning from large and
complex data sets, as well as tools and
technologies to visualize and communicate the
results.

3. Competitive
Business Analysis :
• ´Competitive business analysis is done to
identify both offensive and defensive
opportunities within your industry so you
can develop a sound strategy and take
advantage of possible opportunities, while
dealing with threats in any sized market
space.

4. Steps to Conduct
Competitive
Business Analytics
Analyze Analyze data and plan strategies
Perform Perform SWOT analysis
Analyze Analyze competition and find out market share
Identify Identify current and future competitors
Define Define your market and customers

5. Analyze measures:
Forward and Backward Integration
Economies of Scale
Strength of Buyer/Consumer
Competitor Intelligence
SWOT Analysis
Price Skimming
PESTEL Analysis
Benchmarking

6. Business Intelligence
• Business intelligence (BI) refers to the process of collecting, analyzing,
and transforming data into actionable insights to support business
decision-making. It involves using data mining, statistical analysis, and
visualization techniques to identify patterns, trends, and relationships in
data.

7. Uses of Business Analytics
Supermarkets – BI helps
in gathering
comprehensive data about
customers and shoppers
to determine who may be
candidates for promotional
offers. For example,
pregnant moms can be
targeted for discounts on
diapers or baby items.
Service Providers –
Utility companies use BI to
predict when a customer
will likely transfer to
another service provider
by collating billing
information, website visits,
customer inquiries, and
other such metrics to give
each customer a
probability score, then
offer incentives to
customers perceived to
have a higher risk of
transferring.
E-commerce – You have
probably come across
Amazon’s line “People
who viewed that product
also like this…” which is a
BI-driven, data mining
method to promote cross-
sells and up-sells. This is a
popular technique in online
selling sites

8. Tools of Business Analytics
• SAS
• Python
• Power BI
• Apache Spark
• Tableau

9. SAS
• SAS is a collection of modules that are used to process and analyze data.
• It began in the late ’60s and early ’70s as a statistical package (Statistical
Analysis System).
• SAS is also an extremely powerful, general-purpose programming
language.
• In recent years, it has been enhanced to provide state-of-the-art data
mining tools and programs for Web development and analysis.

10. Why SAS?
• Able to process large data set(s)
• Easy to cope with multiple variables
• Able to track all the operations on the data set(s)
• Generate systematic output
• Summary statistics
• Graphs
• Regression results
• Most government agencies and private sectors use SAS

11. Python
Python is object-oriented
Indentation
Open source
It’s Powerful
It’s Portable
6. It’s easy to use and learn
7. Interpreted Language
8. Interactive Programming Language
9. Straight forward syntax

12. • Python is a high-level, general-purpose programming language. Its
design philosophy emphasizes code readability with the use of significant
indentation.
• Python is dynamically typed and garbage-collected. It supports
multiple programming paradigms,
including structured (particularly procedural), object-oriented
and functional programming. It is often described as a "batteries included"
language due to its comprehensive standard library.
• Guido van Rossum began working on Python in the late 1980s as a
successor to the ABC programming language and first released it in 1991
as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in
2008, was a major revision not completely backward-compatible with
earlier versions. Python 2.7.18, released in 2020, was the last release of
Python 2.

14. Power BI
• Microsoft makes Power BI among the many business analytics tools. It
offers dynamic visualizations with self-service business intelligence
features, allowing end users to create dashboards and reports
independently without assistance.

15. Apache Spark
• Apache Spark is an open-source unified analytics engine for large-scale
data processing. Spark provides an interfacefor programming clusters
with implicit data parallelism and fault tolerance. Originally developed at
the University of California, Berkeley’s AMPLab, the Spark codebase was
later donated to the Apache Software Foundation, which has maintained it
since.

17. Tableau
• Tableau is a visual analytics platform transforming the way we use data to
solve problems—empowering people and organizations to make the most
of their data.

18. THANK YOU